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The following three sections mirror as much as possible the documentation for PyStan:

• Getting Started
• Optimization in Stan
• Avoiding recompilation of Stan models

Some useful tips

Before running

• Compiling Stan components from Matlab
• Compilation options
• Setting Stan parameters
• Setting initial conditions for parameters

While running

• Printing progress information to screen
• Using listeners to notify when sampling is complete
• Blocking the Matlab command line
• Stopping model fitting early

After running

• Extracting samples from StanFit object
• Printing CmdStan's fit summary

Examples

• Linear regression
• Logistic regression
• Multivariate gaussian mixture model
• Watanabe-Akaike information criterion (WAIC)

Aki Vehtari has translated the examples from Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3) into Matlab, with some examples using MatlabStan here.

Troubleshooting

• Solving issues with GLIBCXX and libstdc (Linux)